Calculate a normalised risk ration from proportions — normalise

This assumes case distribution proportions are stratified by a population grouping, e.g. geography or age, and we have estimates of the size of that population during that time period. Normalising by population proportion allows us to compare groups.

Usage

normalise_proportion(
  modelled = i_proportion_model,
  base = i_baseline_proportion_data,
  ...
)

Arguments

modelled

Model output from processing the raw dataframe with something like proportion_locfit_model

A dataframe containing the following columns:

time (as.time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`
proportion.fit (double) - an estimate of the proportion on a logit scale
proportion.se.fit (double) - the standard error of proportion estimate on a logit scale
proportion.0.025 (proportion) - lower confidence limit of proportion (true scale)
proportion.0.5 (proportion) - median estimate of proportion (true scale)
proportion.0.975 (proportion) - upper confidence limit of proportion (true scale)

No mandatory groupings.

No default value.

base

The baseline data must be grouped in the same way as modelled.

A dataframe containing the following columns:

baseline_proportion (proportion) - Size of population

No mandatory groupings.

No default value.

...

not used

Value

a dataframe with incidence rates per unit capita. A dataframe containing the following columns:

time (as.time_period + group_unique) - A (usually complete) set of singular observations per unit time as a time_period
proportion.fit (double) - an estimate of the proportion on a logit scale
proportion.se.fit (double) - the standard error of proportion estimate on a logit scale
proportion.0.025 (proportion) - lower confidence limit of proportion (true scale)
proportion.0.5 (proportion) - median estimate of proportion (true scale)
proportion.0.975 (proportion) - upper confidence limit of proportion (true scale)
risk_ratio.0.025 (positive_double) - lower confidence limit of the excess risk ratio for a population group
risk_ratio.0.5 (positive_double) - median estimate of the excess risk ratio for a population group
risk_ratio.0.975 (positive_double) - upper confidence limit of the excess risk ratio for a population group
baseline_proportion (proportion) - The population baseline risk from which the excess risk ratio is based

No mandatory groupings.

No default value.

Examples




tmp = growthrates::england_covid %>%
  growthrates::proportion_locfit_model(window=21) %>%
  growthrates::normalise_proportion(growthrates::england_demographics) %>%
  dplyr::glimpse()
#> Rows: 26,790
#> Columns: 17
#> Groups: class [19]
#> $ class                  <fct> 00_04, 00_04, 00_04, 00_04, 00_04, 00_04, 00_04…
#> $ time                   <time_prd> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, …
#> $ proportion.fit         <dbl> -13.433629, -13.178345, -12.898497, -12.600007,…
#> $ proportion.se.fit      <dbl> 51.598289, 49.954079, 48.024633, 45.878749, 43.…
#> $ proportion.0.025       <dbl> 1.759164e-50, 5.698079e-49, 3.308357e-47, 2.991…
#> $ proportion.0.5         <dbl> 1.465037e-06, 1.891110e-06, 2.501801e-06, 3.371…
#> $ proportion.0.975       <dbl> 1.0000000, 1.0000000, 1.0000000, 1.0000000, 1.0…
#> $ relative.growth.fit    <dbl> 0.24102860, 0.24048966, 0.23901181, 0.23680352,…
#> $ relative.growth.se.fit <dbl> 1.2309119, 1.2257057, 1.2114298, 1.1900979, 1.1…
#> $ relative.growth.0.025  <dbl> -2.1715143, -2.1618494, -2.1353470, -2.0957455,…
#> $ relative.growth.0.5    <dbl> 0.24102860, 0.24048966, 0.23901181, 0.23680352,…
#> $ relative.growth.0.975  <dbl> 2.6535715, 2.6428288, 2.6133706, 2.5693525, 2.5…
#> $ population             <dbl> 3077000, 3077000, 3077000, 3077000, 3077000, 30…
#> $ baseline_proportion    <dbl> 0.05447011, 0.05447011, 0.05447011, 0.05447011,…
#> $ risk_ratio.0.025       <dbl> 3.229595e-49, 1.046093e-47, 6.073711e-46, 5.491…
#> $ risk_ratio.0.5         <dbl> 2.689616e-05, 3.471831e-05, 4.592981e-05, 6.190…
#> $ risk_ratio.0.975       <dbl> 18.35869, 18.35869, 18.35869, 18.35869, 18.3586…

plot_growth_phase(tmp)
#> Coordinate system already present. Adding new coordinate system, which will
#> replace the existing one.